Case Study: How Finovate Replaced GPT-Powered Scoring with Cruxstack to Scale Trait Intelligence
Company Details
Company: Finovate
Industry: Fintech
Use Case: Scalable churn prediction and upgrade targeting
Stack Comparison
❌ The Problem with Their GPT-Based Workflow
Finovate's product growth team initially used a combination of Mixpanel data, OpenAI's GPT, and MoEngage campaigns to infer user traits like churn risk or upgrade readiness. While powerful in theory, the setup became a bottleneck:
Pain Points from Finovate's Team
Each use case needed new prompts and logic tuning
Every new trait required prompt engineering and testing, creating delays and inconsistencies.
Trait logic was locked inside code or prompt templates
Business teams couldn't modify or understand how traits were calculated.
Traits couldn't be reused across systems
Each integration required custom orchestration and maintenance.
Rebuilding orchestration for every experiment
Testing new approaches required significant engineering effort.
GPT tokens + retries = growing runtime costs
As usage scaled, costs became unpredictable and expensive.
Why They Switched to Cruxstack
Finovate adopted Cruxstack to standardize and scale their trait strategy. The team chose Cruxstack because:
Traits like likely_to_churn, power_user, and ready_to_upgrade came predefined and explainable
They could connect Mixpanel and MoEngage with zero engineering after week 1
Traits were versioned and owned by product/growth, not just by devs or LLMs
Cruxstack offered flat, predictable cost with scheduled scoring
The team could use traits across Salesforce, dashboards, and campaign tools — not just one output
🚀 Finovate's Rollout Plan
| Week | Activity |
|---|---|
| Week 1 | Connected Mixpanel event stream, picked initial traits |
| Week 2 | Verified trait output in MoEngage and Salesforce |
| Week 3 | Launched first churn-prevention playbook |
| Week 4 | Reviewed scores and collaborated on thresholds |
🔁 Key Differences They Experienced
| Feature | GPT + Mixpanel + MoEngage | Cruxstack |
|---|---|---|
| Trait Ownership | Devs + Prompt Engineers | Product/Growth Team |
| Transparency | ❌ Opaque (black-box prompts) | ✅ Fully explainable traits |
| Runtime Cost | GPT tokens + retries | Flat & scalable |
| Integration Effort | High | Low — traits flow where needed |
| SLA | ❌ None | ✅ Scheduled jobs with retry logic |
| Team Alignment | Centralized + opaque | Collaborative + owned |
🧠 Traits in Action at Finovate
likely_to_churn
Triggered MoEngage retention journeys
ready_to_upgrade
Routed to AEs via Salesforce
power_user
Personalized dashboard experiences
segment_fit:fintech_cxo
Filtered leads in CRM
📈 Results After 1 Month
50% reduction in time to launch new traits
No more prompt engineering or custom orchestration needed
Zero dependency on dev or data science after setup
Product and growth teams can manage traits independently
Campaign CTR up 22% when triggered by trait scores
More accurate targeting leads to better engagement
Product team reviews trait definitions quarterly, not weekly
Stable, reliable traits require less maintenance
🧪 Design Partner Feedback
"We moved from a duct-taped LLM stack to a system we could trust and scale. Cruxstack gave us reusable intelligence, not just smarter scripts."
— Head of Product Ops, Finovate